{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d6dd4288b0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([3,5,7,6,2,6,10,15])\n",
    "plt.plot(x,y,'r')# 折线 1 x 2 y 3 color\n",
    "plt.plot(x,y,'g',lw=10)# 4 line w\n",
    "# 折线 饼状 柱状\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([13,25,17,36,21,16,10,15])\n",
    "plt.bar(x,y,0.2,alpha=1,color='b')# 5 color 4 透明度 3 0.9\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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